Variability in Prevalence of Anxiety and Depression Among Visible Minorities in Canada: A Systematic Review, Meta-analysis, and Meta-Regression.
Bibliographic record
Abstract
Background: Depression and anxiety disorders are two of the most prevalent types of psychiatric illnesses, and they significantly impact the overall burden of mental illness worldwide. Millions of visible minorities prefer to migrate to Canada due to its friendly immigration policies; however, they start having emotional and mental health problems soon after they arrive in Canada. Understanding the prevalence of anxiety and depression among Canadian visible minorities is essential to devising appropriate mental health interventions and cultivating culturally sensitive solutions for them. This paper proposes a systematic review and meta-analysis of the variability in the prevalence of anxiety and depression among visible minorities in Canada. Objective(s): The objectives of this study are to conduct: (a) a systematic review of cross-sectional and longitudinal studies, (b) a meta-analysis of the prevalence of anxiety and depression, and (c) a metaregression of variability of the prevalence of anxiety and depression. Methods: We will search academic databases for quantitative research articles using specific search terms. Two independent reviewers will screen articles based on titles, abstracts, and full texts. Citation tracking will locate additional articles. Relevant data on anxiety and depression prevalence will be collected. We will assess the quality of the selected study using the JBI critical appraisal checklist for studies reporting prevalence data. We will perform a meta-analysis to obtain a pooled estimate of the prevalence of anxiety and depression. Also, a meta-regression will be performed to identify the source of heterogeneity in the pooled prevalence estimates. Results: The systematic review will provide a comprehensive, evidence-based overview of the prevalence of anxiety and depression among visible minorities in Canada. We will summarize data in a table based on the study characteristics, such as study location, data sources, sample, type of immigrant group, the prevalence of anxiety and depression, and their measurement tools. The aggregate prevalence of anxiety and depression will be demonstrated using a forest plot. Conclusion: This research will help us gain a comprehensive understanding of existing research, aiding in identifying the aggregate prevalence of anxiety and depression in visible minorities in Canada to promote appropriate health interventions and policies for improving their mental health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.058 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".